With rapid development of the science and technology, machine learning and deep learning have become important topics of science and technology studies, and have been widely used in various fields, e.g., face recognition, autonomous-driving, automatic speech translation and so on. In the process of building a desired model through machine learning and deep learning, the developers must set features to be retrieved from raw data and select a model algorithm to be trained manually and by experience before training a model, and adjust all parameters (e.g., basic parameters and hyperparameters) related to the model algorithm during the training.
However, in the current model training process, only the basic parameters (e.g., weights and biases of the convolution layer or the full connected layer) can be automatically adjusted by the machine learning and deep learning, while most of hyperparameters must be set manually and by experience and need be adjusted through repeated attempts. Because there is a large number of optional features and model algorithms and the adjustment of the hyperparameters still need be accomplished manually through repeated attempts, the developers have to consume a lot of time in selecting features that need to be retrieved and model algorithms that need to be trained and adjusting the hyperparameters in order to build an appropriate model. Furthermore, it is difficult to evaluate whether the built model is an optimization model.
Accordingly, a model building mechanism for building an optimization model is needed in related applications such as face recognition, autonomous-driving, automatic speech translation or the like.